Adopting Lean Industry 4.0: insights from Spanish manufacturing SMEs in an international context.
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| Title: | Adopting Lean Industry 4.0: insights from Spanish manufacturing SMEs in an international context. |
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| Authors: | Guerrero, Blanca1 (AUTHOR), Mula, Josefa1 (AUTHOR) fmula@cigip.upv.es, Poler, Raul1 (AUTHOR), Hines, Peter2 (AUTHOR), Kumar, Maneesh3 (AUTHOR) |
| Source: | International Journal of Production Research. Jun2026, Vol. 64 Issue 11, p4524-4539. 16p. |
| Subjects: | Lean management, Human capital, Production management (Manufacturing), Government aid |
| Geographic Terms: | Spain, Europe |
| Abstract: | Lean approaches and Industry 4.0 (I4.0) offer a wide horizon of possibilities for improving production management efficiency. This study aims to analyse the state of lean I4.0 (LI4.0) in Spain's SMEs and compare it to the international context, i.e. other European countries and outside Europe. It analyses seven different aspects (departments involved, implementation maturity, motivations, maturity assessments and roadmap, enablers and challenges, people skills and competences, and institutional support) by a qualitative 29-question open-ended survey, answered by 179 Spanish SMEs, 123 from other European countries and 80 global companies, all from the secondary sector with fewer than 250 employees. The main findings highlight the need for context-specific approaches for LI4.0 adoption, noting that Spain's approach differs from broader European and global trends by implementing lean and I4.0 simultaneously. Key enablers include employee commitment and a stronger focus on market needs. Increased government support could enhance adoption in Spain, while global efforts seek to address post-implementation knowledge gaps and integration support. These outcomes can provide SMEs with future research and effective support in their search for efficiency through LI4.0, and also guide third-party support agencies, particularly national and regional governmental bodies. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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